Working Group on Energy Forecasting and Analytics

IEEE Working Group on Energy Forecasting and Analytics
–Energy forecasting:
-Forecasting objectives: load, renewable energy, price; individual consumer load, demand response, EV charging load, net load, wind power ramp; power, gas, heat, and cooling demands; reserve capacity, risk, network congestion;
-Forecasting algorithms: traditional regression, advanced machine learning, deep learning, transfer learning, ensemble learning, robust forecasting;
-Forecasting outputs: point forecasting, probabilistic forecasting, hierarchical forecasting, cost-oriented forecasting;
-Forecasting evaluation: Alternative loss functions for different forecasting objectives and different applications.
–Energy Analytics:
-Data preprocessing: outlier detection, data cleansing, feature selection, data compression;
-Behavior modeling: load profiling, energy theft detection, renewable energy spatiotemporal correlation analysis, pattern recognition, sensitivity analysis, load or renewable energy simulation;
-Applications: demand response implementation, data-driven pricing, bidding, and trading, topology identification, outage and risk management, privacy concerns.

-Chair: Dr.Jethro Browell, University of Glasgow

-Vice-chair: Dr. Yi Wang, ETH Zurich, Switherland



Prof. Hamid Zareipour, University of Calgary, Canada (Secretary: 2012 – 2016; Vice Chair: 2016-2019)

Prof. Tao Hong, University of North Carolina at Charlotte, US (Chair: 2011-2019)
-Past Officers: Dr. Shu Fan, Monash University (Vice Chair: 2011-2016)

Past activities:

Recent Activities:

  • IEEE PESGM 2020 Panel session on “Machine Learning Applications to Energy Forecasting and Analytics”
  • Participation in the ERPI webinar 2020, where Prof. Hamidreza Zareipour talked about the EPRI Customer Research Program. There is a load forecasting interest group and about 400 people attended this meeting with a focus on long-term load forecasting.
  • Review paper: T. Hong, P. Pinson, Y. Wang, R. Weron, D. Yang and H. Zareipour, “Energy Forecasting: A Review and Outlook,” in IEEE Open Access Journal of Power and Energy, vol. 7, pp. 376-388, 2020. (Best Paper of the IEEE OAJPE for 2021)
  • Day-Ahead Electricity Demand Forecasting Competition: Post-COVID paradigm (
  • IEEE OAJPE special section on “COVID-19 Impact on Electrical Grid Operation: Analysis and Mitigation” and competition paper
  • IEEE TSTE special section on “Advances in Renewable Energy Forecasting: Predictability, Business Models and Applications in the Power Industry.”
  • ICHQP2022 conference Special Session on “Forecasting and Analytics for Power Quality Problems.”

Future Plan:

  • IEEE PESGM 2022 Panel session on “Utilisation of probabilistic energy forecasts in power system operation”
  • IEEE PESGM 2022 Tutorial on “Probabilistic Energy Forecasting: Methodologies, Implementations, and Applications”
  • Sustainable Energy, Grids and Networks, Special issue on “Forecast production and end-use for efficient management of energy systems”
  • Host energy forecasting competition (net load, multi-energy etc.)